Comparative Study on the Cooling Effects of Green Space Patterns in Waterfront Build-Up Blocks: An Experience from Shanghai

Yunfang Jiang, Shidan Jiang and Tiemao Shi
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Yunfang Jiang: Center for Modern Chinese City Studies, School of Urban and Regional Science, East China Normal University, Shanghai 200062, China
Shidan Jiang: Center for Modern Chinese City Studies, School of Urban and Regional Science, East China Normal University, Shanghai 200062, China
Tiemao Shi: Institute of Spatial Planning and Design, Shenyang Jianzhu University, Shenyang 110168, China

IJERPH, 2020, vol. 17, issue 22, 1-29

Abstract: Different structural patterns of waterfront green space networks in built-up areas have different synergistic cooling characteristics in cities. This study’s aim is to determine what kinds of spatial structures and morphologies of waterfront green spaces offer a good cooling effect, combined with three different typical patterns in Shanghai. A multidimensional spatial influence variable system based on the cooling effect was constructed to describe the spatial structural and morphological factors of the green space network. The ENVI-met 4.3 software, developed by Michael Bruse at Bochum, German, was used to simulate the microclimate distribution data, combined with the boosted regression tree (BRT) model and the correlation analysis method. The results showed that at the network level, the distance from the water body and the connectivity of green space had a stronger cooling correlation. The orientation of green corridors consistent with a summer monsoon had larger cooling effect ranges. In terms of spatial morphology, the vegetation sky view factor (SVF) and Vegetation Surface Albedo (VS Albedo ) had an important correlation with air temperature (T), and the green corridor with a 20–25 m width had the largest marginal effect on cooling. These results will provide useful guidance for urban climate adaptive planning and design.

Keywords: green space network; green space pattern; spatial morphology; cooling effect; ENVI-met simulation; boosted regression trees (BRT); marginal effect (ME); Shanghai (search for similar items in EconPapers)
JEL-codes: I I1 I3 Q Q5 (search for similar items in EconPapers)
Date: 2020
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (6)

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